A Survey on Hidden Markov Model for Credit Card Fraud Detection
Anshul Singh1, Devesh Narayan2
1Anshul Singh, Computer Science and Engineering Department RCET Bhilai, CSVTU India.
2Devesh Narayan, Computer Science and Engineering Department. RCET Bhilai, CSVTU, India.
Manuscript received on january 17, 2012. | Revised Manuscript received on February 05, 2012. | Manuscript published on February 29, 2012. | PP: 49-52 | Volume-1 Issue-3, February 2012. | Retrieval Number: C0190021312/2011©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Credit card frauds are increasing day by day regardless of the various techniques developed for its detection. Fraudsters are so expert that they engender new ways for committing fraudulent transactions each day which demands constant innovation for its detection techniques as well. Many techniques based on Artificial Intelligence, Data mining, Fuzzy logic, Machine learning, Sequence Alignment, decision tree, neural network, logistic regression, naïve Bayesian, Bayesian network, metalearning, Genetic Programming etc., has evolved in detecting various credit card fraudulent transactions. A steady indulgent on all these approaches will positively lead to an efficient credit card fraud detection system. This paper presents a survey of various techniques used in credit card fraud detection mechanisms and Hidden Markov Model (HMM) in detail. HMM categorizes card holder’s profile as low, medium and high spending based on their spending behavior in terms of amount. A set of probabilities for amount of transaction is being assigned to each cardholder. Amount of each incoming transaction is then matched with card owner’s category, if it justifies a predefined threshold value then the transaction is decided to be legitimate else declared as fraudulent.
Keywords: Credit card, fraud detection, Hidden Markov Model, online shopping.